{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"/Users/admin/Desktop/day02/diabetes/diabetes.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
       "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
       "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
       "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
       "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
       "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
       "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
       "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
       "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
       "\n",
       "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
       "count  768.000000                768.000000  768.000000  768.000000  \n",
       "mean    31.992578                  0.471876   33.240885    0.348958  \n",
       "std      7.884160                  0.331329   11.760232    0.476951  \n",
       "min      0.000000                  0.078000   21.000000    0.000000  \n",
       "25%     27.300000                  0.243750   24.000000    0.000000  \n",
       "50%     32.000000                  0.372500   29.000000    0.000000  \n",
       "75%     36.600000                  0.626250   41.000000    1.000000  \n",
       "max     67.100000                  2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data.groupby(\"Outcome\").size()\n",
    "# data['Outcome'].value_counts()\n",
    "sns.countplot(data.Outcome);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pregnancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1acb4d68>"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 查看怀孕次数的分布\n",
    "sns.countplot(data.Pregnancies)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "怀孕次数十几次的还不少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1ab64080>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='Pregnancies', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，怀孕次数高的人群中得病的人占比较大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Glucose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a345d30>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 查看Glucose的分布\n",
    "sns.distplot(data.Glucose,kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a305588>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Glucose', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，Glucose浓度高的人大部分都是糖尿病。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BloodPressure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bb50390>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6/q8F/qg/++Yo4PFNQzzzUbovYToHOLGqfjbhpmuB05LslOQAujeX/3PWCqmqkf0B3kD3TvZ/A++b63q2oe5X0/2bdjtwW//zBrox7lXAuv5yj7mudcD9eS3wxf76i/pf2PXA54Cd5rq+Aeo/DFjTH48vALuP4rEA/gq4B7gT+Gdgp1E4HsBn6d5X+BVdT/fMqZ5/uiGPj/Wv+TvozjKa833Ywn6spxuL3/Q6/8SE9d/X78e9wPGzWZufjJWkxo3y0I0kaQAGvSQ1zqCXpMYZ9JLUOINekhpn0GteSvJUktuSfDvJLUle2bcvnjg74JDbuCHJ0v76A0nu6Lf3tSS/NRPbkOYDg17z1c+r6rCqOpRuxr+/2w7bfF2/vTXAeze/cTZnF5zLbal9Br1GwfPppth9mn6+9U/1PfFbk7xuK+07J7m8nxv8CmDnKbZ3I/A7/X1+kuSvk6wGjk7ye0m+keTmJF+d8DH9tye5u3/sy/u23+//K7mtr+M3krw2/dz9/Tr/lOQt/fUHknwgyTeBU5O8OMlX+m39e5IDZ+j51LPMrHxnrDQDdk5yG/A8YBHdXDqbOwugqn63D8GvJXnJFtr/DPhZVR2S5BDglim2fQLdpy6hmxzszqr6QD8/0TeAk6pqPMkfAn9LNyvhucABVfXLCV8u8RfAWVX1rX4Cu18MsN+/qKpXAyRZBfxpVa1LciTw8SmeB2mLDHrNVz+vqsMAkhwNfCbJwZut82rgHwGq6p4k3wVesoX21wAX9u23J7l9s8e7PslTdFMhvL9ve4pu8jmAlwIHA9d108mwA91H3unvc2mSL9BNowDwLeBDSS4Frq6qjf39tuSKfp93o5uU7HMT7rPT1u4sTcag17xXVf+RZE9gbLObpkrNLaXplub8eF1V/WCztl9U1VMTHveuqjp6kvu+ke4PyYnAXyZ5eVWdn+Tf6OYxuinJ6+mmqp04ZPq8zR7np/3lc+jmkj9sC/VKA3GMXvNeP/yyA/A/m910I3BGv85LgP3pJogapP1g4JBtLOVeYKz/D4MkOyZ5eZLnAPtV1fV0X8SyENgtyYur6o6quoDuDd4Dge8CB/WzFv4m3SyTz1Dd9xPcn+TUfltJcug21isB9ug1f20ao4euJ72sqp7abOjj48AnktxB11N+Sz9GPlX7RXTfJLVp1tBtmha2qp5IcgpwYR/SC+jm4P8O8C99W+i+o/WxJH/TvxH8FN2UwV/u67iSbqhnHXDrFjZ5BnBRkvcDO9LN+//tbalZApy9UpJa59CNJDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXH/B9NAWOKRq+xAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.BloodPressure, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知，BloodPressure值为0的缺失值需要填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a3059b0>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BloodPressure', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SkinThickness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b1f1668>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.SkinThickness, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为0的值比较多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b1e04a8>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='SkinThickness', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知，相关性不是很强"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Insulin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1762c6a0>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.Insulin, kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知，Insulin血清胰岛素为0的缺失值比较多，需要插补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1b1ef2b0>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Insulin', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17728e80>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xR4AvAG8A944gnzQUTr9on1NV/wisB6bm7PsRsB24gcGqldJEcqSufU6Sk4H9GCyedNCch/4E+Luq+v5gAUtp8ljq2lfsmVOHwYdTbK6q9+aWd1U9h1e9aMK5SqMkNcQ5dUlqiKUuSQ2x1CWpIZa6JDXEUpekhljqktQQS12SGmKpS1JD/hcrTevRwP8/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.BMI, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10530fb38>"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rujTuj7oI/Ff3UgqLDjJ37lxrC3SABoFFNm/eTG5ODlWtetX8FB3Hm9qbgvx8vWmslM0WL17M92vWcF2XUlpEN36hqB4tfFzctpK3336bLVu2WFih/TQILLJw4UIk0kV1y+71ft2X1AmJjmXhwoU2V6ZU+MrPz+e5Z2fSK8nHJe1PfUr467uX0yI6wGOPPnJ0jZGzgQaBBcrKyvhs6VKqkruC6wRjBSIiqGzZnRUrVpCfX+/gaqVUEzLG8MTj0/BVVXBb7xIiTmP58Lgow7hexezYuYs5c+Y0fZEO0SCwwNKlS/FWVR2dUuJEqlv1xBjDxx9/bFNlSoWvDz74gMxVq7m+WymtYxu+QXwiA1tW8x/tKpk//03WnSUT0mkQNDFjDO9/8AEmriWBuFYn39ediD+xHQv/uUhvGitlob179zJr1gv0T6nm0tO4JHS8G7qX0cpjmPbYo2fFqoMaBE0sKyuLbVu3nvAm8fG8ab0pKizgm2++saE6pcKPMYbpTz+F+L2M613amF/LBnlccGuvYvbu239W9CLSIGhib731FhIVQ3XLbo3a35fUEdwJzH/rLYsrUyo8LV++nMxVq7muaxkt3ad/Seh4fZN9DGtbyVtvvdXsp5bXIGhCW7du5cuvvqKyVW+IjGrcQRJBZVpfsjZsYPXq1dYWqFSY8Xq9vDh7Fh3iAwxv3/AI4lP1y27lxEQG+Nvs2U3+3nbSIGgixhhmzZ6NuKLxtjnnlI6tTu0FMfG8MGuW3itQqgl9+OGH7DuQzy+7lZ5WL6GGJEQbRnco45tvv2XDhg1NfwKbaBA0kaVLl5L5r39R0WYQuGJO7eAIFxXtzyM3J4d3333XmgKVCjM+n483//EGPVr46J/y74vONJWfdagkPhpef/01y85hNQ2CJnDgwAFmPDOTQHwa1a37nNZ7+FK64kvqwIt//zvbt29v4gqVCj/Lli0jv6CQ0Z3Km+QG8YnERMLP2pezYsV3bNu2zboTWUiD4AxVVVUxZcqDlFdWUd7lJz/MMnqqRKjsdCE+XDzwwBRKSkqatlClwogxhrfmv0n7+ACDWlrXGqg1PL2SmEhYsGCB5eeyggbBGfD5fDz88MPk5uZQ1mUYxt3ijN7PRMdS1vUS9h3Yz4MPPnhW9E9WygkrV65k67btjEi3tjVQKyHK8JO2FXy2ZAn79++3/oRNzPIgEJFIEfleRBYFX3cRke9EJEdE3hKRZrleYyAQ4Omnn+bLL7+kssOP8Cd1bJL39Se0pqLzT1i7di1/mTr1rF0IQymrBAIB/v73F0mNNVzYxr4PU5d3rATjZ968ebads6nY0SKYCGyq8/pxYIYxpgdwCLjVhhqaVCAQ4KmnnuLjjz+mqt0gqtv0a9L397XsRmXHoXzz9df8+c9/prra+qatUmeLTz/9lNzcrVzTqRSXjdc8WroDXNqugsWffEJ2drZ9J24Clv43iUg6cDnwUvC1AJcCbwd3mQdcbWUNTc3n8zFt2jQ++ugjqtoNwtvuXEvOU926L5Udh/LVV1/xxz/+US8TKdUIhYWFPP/cs/RM8vHjNvbPDnpNlwoSY2qmnmhOH+CszstngPuA2uF8LYHDxpja6x15QPv6DhSRO0QkU0QyCwoKLC6zcaqqqvjTn/7Ep59+SlX7wXjbD27UNBKnq7p1Xyo7/ZgVK1Zw3333UVZWZtm5lGruqqureWjqVLyV5ac9u+iZiosy3NKzmG3bd/DCCy/YX8BpsiwIRGQ0kG+MWVV3cz271rsyhDHmRWNMhjEmIzU11ZIaT0VZWRn3T57M119/TWXHoXjbDbLlvNVpvanoejFr161n4t13c/jwYVvOq1RzYozhqaeeYu26dYzrVUKbM5hd9Eyd26qakR0qeO+993jnnXccq+NUWNkiuBC4UkR2APOpuST0DJAkIq7gPunAXgtraBKHDx9m4t13s2bNWiq6DKO6dV9bz+9r2Y3y7sPZum07d44f3yx7JShlFWMMs2bNYvHixVzTpfy0Lwm9nh3LzpJIdpZE8ujqRF7Pjj3tmn7ZvZzzWnl5/vnnmsXa5JYFgTHmD8aYdGNMZ+B6YJkxZgzwOXBtcLebgQ+sqqEpHDhwgDvHj2frtu2Udx+Or1X9K45ZzZ/UgbIeP2ffgULuHD+eHTt2OFKHUqEkEAgwY8YMFixYwE/TK7i6c8Vpv9euUhcV/ggq/BFsPhzFrlJXwwedQITAf/croV9yNdOmTQv5lQidGEdwP3CviORSc8/gZQdqaJSdO3dy553j2XegkLIeP8Of1MHRevwJrSntNZJDJRVMuOsuNm/e7Gg9SjmpqqqKhx56iIULF3J5xwp+1cOeMQONFRMJd/cvZlArL9OnT2fu3LkY0/g1ku1kSxAYY5YbY0YHn28zxpxvjOlujLnOGBOS3WFycnKYcNddHCwpo7TXCPwJbZwuCYBAbAolvUZRWk3wctUap0tSynYHDx7knrsnsvzzz/lltzJ+0S20QqBWdCT89pwSLmpTydy5c3nkkUdCsgegjiyuR1ZWFr+dOJGSqgAlvUYRiG3pdEnHMO5ESnuNolLcTJo0ie+++87pkpSyTU5ODv/96zvIzd7ChHNKuLxTZUiGQC1XBNzep4xru5bz2Wefcc/dEyksLHS6rGNoEBzn+++/5957f0eFiaK016gznjbCKiY6jrJeI/FGJ/LAAw/w5ZdfOl2SUpb77LPPmDD+TnwlhUw59xBD0uwfK3A6RODKzhXcdU4JW3O28OvbbyMrK8vpso7SIKgjMzOT++6/n8pID6U9R2Ji4p0u6aRMlIfSniOo9rTkT3/6E8uXL3e6JKUs4ff7mT17Ng8//DCdYyuYmnGQLonNb+2OIWle/mfwISKrDnH3xN/y0UcfOV0SoEFwVGZmJn/4wx/wuuIp6zkCE336Xcds5YqhrOfP8cWlMnXqVD7//HOnK1KqSZWUlDB58v3Mnz+fS9tXcv+gIyRGh+ZN18boEO/nz+cdoldiJU888QQzZ850fE4xDQJg7dq1NSEQlVATAlEep0s6NZFRlPX4Gb64NB566CG++uorpytSqkns27eP8Xf+htWrMrmlVylje5XZOn+QVeKjDL8bUMyI4MCzyZPvp7y83LF65GTdmUTk3pMdbIyZ3uQV1SMjI8NkZmZa8t5btmxh4sS7qYxwU2pRCMTsWkFUYQ4A/tiWBGJTqOo4tMnPg7+auOzFRFUe5InHH+e8885r+nMoZZOcnBzuv28SlaWHufucI/ROtvZT86OrE9l8+Ie1xnsnVfPA4GJLzwnwxd4Y5myJp1u3bkx7/Alatmy6zikissoYk9HQfg1la0ID/5q1/Px87p88mUpclPb4mWUtgYjyg4i/GvFX4yrZT0T5QUvOU9My+Cm+6EQe/OMfddCZarZyc3O5e+JvkcrDPHjuYctDwEkXt6vingHF7NqxjYm/vcuRaWROOnTOGPMXuwqxm9frZfIf/sCRkjLKel+OiY5zuqSm4YqhrPtlRGxexH33T+aVl18iPj60b3orVdfevXu5b9LviQlUMGXwIVq5nZs3yC4DW1bz+wGHeWKt4f77JjHjmZnExtp3n/KkLQIRefZk/+wq0govvfQS27ZupazLMAKeZKfLaVImJp7SrpeQn3+AmTNnOl2OUo3m8/n444NT8JYdZtLAw7aGQIVPcLvdXHvttbjdbip89g5O6JXkY0K/YnJycnjmmWdsPXdDl4b+G7iImonhMoFVx/1rljZv3syCBQvwpvZuspXFQk0goTVVbQexZMkSVqxY4XQ5SjXKP//5T7Zu287YniW0j7O3e2i5Txg9ejQTJkzg8ssvp9zmIICamUsv71jOp59+yvr16207b0NB0BZ4Efg5cCMQBSw0xswzxjS/9dj4YaZCifZQ1WGI0+VYytt2ALgTmTV7Nn5/8+tzrcKLMYZ5c+bQN8XHkFT7B4rFugyLFi3iueee48MPPyTW5UwX1Ss7V5Dshnnz5tp2zpMGgTGmyBgz2xhzCTAWSAKyRORGO4qzwpYtW1i7di0VrQdAZFTDBzRnEZFUtDuXnTt2sHLlSqerUeqk9u3bx+HiYn6U6syUER6XobKyknfeeYfKyko8DgVBTCSc27KSTRs32jZJXaN65IrIYOBu4FfAxzTjy0Iff/wxEuGiulUPp0uxhS+5MxLt4eOPP3a6FKVOavfu3QC0jdXWa5tYP2XlFRw8aFEPw+OctNeQiPwFGE3N4vPzgT/UWWay2THG8OVXX+NtkQ6uaKfLsUdEJFVJnfjuu5VUV1cTFXWWt4JUs9WxY839uryyyLO6u2hj7CmNJCEuluRkezqyNNQi+CPQAhgIPAasFpF1IrJeRNZZXl0Ty8vL42BRIf7Edk6XYit/YjuqqirZtGmT06UodUJt2rQhtVVLVuS7CTTfGSTOWIVPWHvQTf+Bg4iIsGcYdUNn6QIMp6ZVMBq4Iviv9nmzUvuH0B/f2uFK7FX7/epCNiqUiQg3j72F7MMulu+Ncbocx8zPjaXYK9x4o323YhsaULbz+G0i0gooMqG61M5JZGdnI5EuAp7QnFraKibKg8TEs2XLFqdLUeqkLr/8cj5ftow3135P21g/fcLsEtGyPTF8vtfNL35xHX369LHtvA0NKBsqIstF5F0ROVdENgAbgAMiMqKBY90islJE1opIVvB+AyLSRUS+E5EcEXlLRGy7WJ+Tk4vfkwxyFsxadYqqPclk5+Q4XYZSJyUiTHnwQdq0T+fpdS3YePD01w1ubj7Li2HulniGDv0Rt912m63nbugv4vPAo8CbwDLgNmNMG2AYNfcMTqYKuNQYMxAYBIwQkaHA48AMY0wP4BBw6xnU32iBQIDs7Gx8ntBabcwu/tiW5O3e7egMh0o1RkpKCs/MfJb2HTrx1LoWfL4nhuZ3/aHx/AF4MzeWV7Pj+fEFFzB16kNER9vbmaWhIHAZYz41xvwvsN8YswLAGNPgxWZTozT4Mir4zwCXAm8Ht88Drj6tyk9RXl4eFRXl+ONa2XG6Y/m9xwxdx2//YBl/XCuMMeTm5tp+bqVOVXJyMs/MfJZzz8tgzpZ4Xtoch/cs7FV6uEqYtqYFH+/ycNVVV/GXqVNtDwFoOAjqTvRRcdzXGsxoEYkUkTVAPrAE2AocrtMFNQ9of4Jj7xCRTBHJLCgoaOhUDdq4cSMAgfjUM36vUyU+7zFD18VnfxAE4mq+79r/B6VCXYsWLZg27XFuuukmvtzn5n8yU9hWHGnZ+TrG+/BEBvBEBuidVE3HeGvvT2QWRPNgZgo7yj088MAD3HPPPY51727oAtxAESkGBPAEnxN87W7ozY0xfmCQiCQB7wH13f2oN1CMMS9SM70FGRkZZ9ww3LBhAxIVQ8CddKZvdcqMK5pFixZhjOHDDz/EuOxf/cxEecDTgvXr13P99dfbfn6lTkdkZCTjxo2jf//+PD7tMaauEq7sVM6VnSuafIGaX/UsZ1dpzZ9EK9chKKsWXs+J4+v9MXTv3o0pUx6kS5culp2vMRrqNdQk8WuMOSwiy4GhQJKIuIKtgnRqJrSz3Jq166iOTcWRseuR0VSWH+Sdd96peZ1gfxgBVMelsW7deowxiBP/D0qdpiFDhjBn7jyeffZZ3l+yhNVFbm7tVdzs1i1eVRDFvJxEir0R3HTTr7jxxhtDYpCnZd1nRCQ12BJARDzAZdSMUP4cuDa4283AB1bVUKukpIS83bvCbvzA8fzxrSkpKT46lF+p5iQhIYEpU6bw0EMPUeZK4c+rkpifG0tVM8iCw1XCc+vjmbk+kZS2nXnhhRcYN25cSIQANHxp6Ey0BeaJSCQ1gbPAGLNIRDYC80XkYeB74GULawDqDiSz//5AKPEH7xNs3rz56HB+pZqbn/zkJwwaNIjZs2fz4YcfklnoZlzPYvqmhN6YA2Pgy30xvLk1Hi8ubr/9Fn75y1/icoVWt1jLqjHGrAPOrWf7NuB8q85bn23btgE1XSjDWcDTAiIij/5/KNVcJSQkMGnSJC677DKeevIJpq0RLm5byfXdy4mLCo2+pvkVEczZEk/WwSgG9D+H30+6L2Q/gIVWLFlkz549SJQbXOE7bB2oGUjnTiQvL8/pSpRqEueeey6vzJnL3LlzeeuZwNKuAAASs0lEQVStt1h3qKZ1MLBVtWM1BUzNCOG3tsYTGe3mnnt+wxVXXGHbvEGnI3Qra0KlpaWYqAY7OYUFf2Q0ZWVlTpehVJOJiYnh17/+NbNnzyapTSeeXpfIS5viHFlhrKAigifWtODV7HgGDM5g7rxXueqqq0I6BCBMWgS6OtcPDILPF3rXUpU6Uz179uRvL/6dV199lX+88QabDsdwZ99iurWw5+d9xYFo5mQnIC43v/99cMxQM+mdF9ox1UTatGmDVJZyVo9Tb6Qobylt27Z1ugylLBEdHc1tt93Gc88/T2Riax5e3YJFO62d1rrKDy9viuOFrAS69OjDy6/MYfTo0c0mBCBMgqBHjx6YgI/I0gNOl+IoqTiCqSqle/fuTpeilKX69evH3196mYuGDWPB1jieW59ApQUNg8LKCB5encz/7XczZswYnn32uWb5QSssguCiiy7C44kl6kB4T68Qnb+RiMhILrvsMqdLUcpyCQkJ/PnPf2HChAl8XxTDQ6uTKahouj95OUdc/HlVMgW+WB57bBq33357yHULbaywCAKPx8O11/4/og7twHVoh9PlOCKyZD/R+ZsYNXIkKSkpTpejlC1EhGuvvZYnnnySQyaeqauTySs98wkTvi+MYtr3LUho2ZZZs//G0KFDm6Ba54RFEADcdNNNdO/Rg9gdXxNRVuh0ObaSiiPEbltO27btuPPOO50uRynbZWRk8NcXZuGKS+bRNUlnNHndigPRPLs+ka7de/DCrNl06tSpCSt1RtgEQVRUFA9NnUpqShLx2Z8QWbLftnMHYlMwkVGYyCh8CW0IxNr3iTyi/CAJ2R+R6InmkUceJjbW/gnvlAoFnTp14rnn/0p8chpPrE06rZbBqoIoZm1MoN855zB9xjO0aHF2rHYYNkEA0LZtW55//jnatk4lNvsTovZn2dKTqKrjUPyxLfHHtqSi9yiqOtrTjHQV5hC/eRHJCbE8/9yzdO3a1ZbzKhWq2rVrxzMzn8WTkMyT65Ioqmz8n8Ath128sDGR3r168fgTTxIXF2dhpfYKqyAASEtLY/asWfx46FDcu7/Dk/sZUn2Wrdrlq8K97Qs8279kwDn9+Nvs2SE7tF0pu7Vp04Ynn3oab4SHZ9a3aNSCN0WVEczc0ILWbdrx2LTH8Xg81hdqo7ALAoDExEQeeeQRJkyYgLt0Pwkb3iMqf3PzH2dgDK6ibSRmvUvMoe2MHTuW6dOnk5oa3pPtKXW8rl278sf/+RM7SyJ4I+fkn+x9AfhrViKBSDePTXucpCRnppG3UlgGAfzQm2DOnFcY0K837p3fELflw2Z7Izmi4jCxOZ/i2bac7p078Le//Y2xY8cSGWndik5KNWdDhw7lv/7rv/h8r5vVBSeeDvqfOz3kHolk0n3306FDBxsrtE/YBkGtDh068Mwzz3D//feTJFXEbVxIzPavkOrjV+YMUT4vMbu+Iy7rfeK9h5gwYQKzZ82iR48eTlemVMgbN24cXTt35tWcRCp8NctV1l2icm9ZBP/cGcvw4cO55JJLHKzUWs1z9EMTExFGjhzJRRddxGuvvcbbb79DzOEdVLQZSHXrvhARgp+qTYCogmw8+77HVFdy+ahR3HbbbSQnJztdmVLNhsvl4neTJjFhwngW7fTwq57H3i/8R248bk8s48ePd6hCe4R9i6CuhIQE7rzzTubMeYXzzxuMO+9fJGS9h+vgjpC6fxB5ZA/xGz/AvfMbzundgxf/9jcmTZqkIaDUaejXrx+XXHIpn+6Jo9j7w/xAOUdcrCuKYsyvbjzrB2FauVRlBxH5XEQ2iUiWiEwMbk8RkSUikhN8DLm/Xp06deLxx6fx5JNP0rF1Mp6ty4jNXkxExSFH65LKYjy5nxGbvZg2CTFMnTqVZ2fOpGfPno7WpVRzd/PNN+P1Gz7L+2G6+kU7PSQlJnD11Vc7WJk9rGwR+IDfGWP6ULNo/XgR6QtMBpYaY3oAS4OvQ9KQIUN45eWXmThxIon+YuKy3idm1wrwe+0tJOAjes9qErLeI648n9tvv53XXnuVYcOGNasZDpUKVZ06dWLIkCH83/5YAgYOVkWwpiia0VdeddZ1Fa2PZUFgjNlnjFkdfF5CzcL17YGrgHnB3eYBIR23LpeLa665hjfeeJ0rr7iC6PxNtl4uijyyh4Ss94nZu4ZLL/kPXn/9NcaMGUN0dLTl51YqnIwadTkHK2HTIRff7o/GGBg5cqTTZdnClpvFItKZmvWLvwNaG2P2QU1YiEiaHTWcqaSkJO69915GjBjBk089xfaty/Ald6Sy04WYKAs+Mfi8uHetIKool7bt2vP73/2J8847r+nPo5QC4Ec/+hFRrkjWFkWzs8RF1y6dad++vdNl2cLym8UiEg+8A9xtjCk+hePuEJFMEcksKCiwrsBT1LdvX/7+4ov8+te/JqZkLwkb32/yGU0ji2veN+bQNn71q18xd84rGgJKWczj8dB/wADWHYwhuziKIef/yOmSbGNpEIhIFDUh8IYx5t3g5gMi0jb49bZAfn3HGmNeNMZkGGMyQm1krMvl4oYbbuClv/+dbh3T8eQuI2bntxA4wyUxTYDovFXEbvmE9qlJ/PWvf+W2224jJiamaQpXSp1Unz592VsWgT8Affr0cboc21jZa0iAl4FNxpjpdb60ELg5+Pxm4AOrarBaly5dmD17Fr/4xS+Izt9E3JaPEO9pLgzvqyQ2+1Ni9q1l5MiRvPzSS2H1g6hUKKi7el+3bt0crMReVt4juBC4EVgvImuC2x4ApgELRORWYBdwnYU1WM7lcnHnnXdyzjnn8OhjjxGxeRFl3X96SlNNS2Ux8blLcFWXc+999zFq1CgLK1ZKnUibNm2OPm/durWDldjLsiAwxnwFnKhv43CrzuuUYcOG0b59eybddx+y5SPKul+GP6FNg8dFlB8kPnsxsTEupj0xnf79+9tQrVKqPmlpP/RdCaeeeTqyuAl169aN2bNm0a5NGnG5nx0zgV0gNuXfWglScYT4nMUkJ8Yy64W/aggo5bCEhASnS3CEBkETS0tLY8b06aS2TCE+51OkqgSoWZym7oI0Ul1JfM5iEjwxPDNjhq4XoFQICKdWQF0aBBZIS0tj+tNP4XZFELv1cwj4jt3BBPBsW47LX8UTj0/TEFBKOUqDwCLp6elMmfIAEWWFRO9bd8zXogq2EFm8l7vvnkjv3r0dqlAppWpoEFjooosu4tJLL8W9f/3RS0RSXYlnz2rOPXcwl19+ucMVKqWUBoHlfvOb3xARIUTv3wBAVP4mjK+Ku+6aoBPGKaVCggaBxVJTU7ls+HBiinLBV0VM4RbOP/98unbt6nRpSikFaBDYYuTIkRh/NTH71oK3nBEjRjhdklJKHaVLVdrgnHPOwe2Jhf0biIiIYMiQIU6XpJRSR2mLwAYul4vevWpWEUvv0CFsB60opUKTBoFNau8JdNN7A0qpEKNBYJPaqbTj4+MdrkQppY6lQWATt9vd8E5KKeUADQKllApzGgQ200FkSqlQo0FgEw0ApVSo0iCwmQaCUirUWLlm8Ssiki8iG+psSxGRJSKSE3xMtur8ocoY43QJSil1DCtbBHOB4+dSmAwsNcb0AJYGXyullHKQZUFgjPk/4OBxm68C5gWfzwOutur8oUovDSmlQo3d9whaG2P2AQQf0xrY/6yjl4aUUqEmZG8Wi8gdIpIpIpkFBQVOl3PGtCWglApVdgfBARFpCxB8zD/RjsaYF40xGcaYjNrpGZRSSjU9u4NgIXBz8PnNwAc2n18ppdRxrOw++ibwLdBLRPJE5FZgGvBTEckBfhp8rZRSykGWLUxjjLnhBF8abtU5Q5neJFZKhaqQvVl8ttKbxkqpUKNBYDNtGSilQo0Ggc20RaCUCjUaBDapDQBtESilQo0GgU00AJRSoUqDQCmlwpwGgVJKhTkNApvpzWKlVKjRILBJ7T2CQCDgcCVKKXUsDQKblJWVOV2CUkrVS4PAJoWFhQAcOXLE4UqUUicSrr37NAhssmfPnuDjXocrUUqdiNfrdboER2gQ2GTb9h0A7Nq9C7/f72wxSql6VVRUOF2CIzQIbFBSUkJRYQF+dwuqvd6jrQOlVGgJ13t5GgQ22LJlCwDVqb2Oea2UCi2lpaVHn4dTy12DwAabNm0CoLpVdyTSdfS1Uiq01G0RlJeXO1iJvTQIbLBp0ybwJIHLjS+2FRs3bnS6JKVUPeoGQd3WwdnOkSAQkREiskVEckVkshM12GlLdg7VnhQA/LEt2bptmw4sUyoEaYvAJiISCfwVGAn0BW4Qkb5212GX8vJyigoLCHiSAQh4kqn2etm3b5/DlSmljldZWVnv87OdEy2C84FcY8w2Y4wXmA9c5UAdtigoKAAgEBN/zGPtdqVU6Kiqqjr6XIPAWu2B3XVe5wW3nZVqRxIblzv4GANAcXGxYzUppepXt6dQOF2+dSII6pt+89/GdYvIHSKSKSKZzfnT84lmG9VZSJUKPXWnmNAgsFYe0KHO63Tg3+ZdMMa8aIzJMMZkpKam2lZcU4uJqWkBSKC65tHvAyA6OtqxmpRS9YuMjDz63OVyOViJvZwIgn8BPUSki4hEA9cDCx2owxa1ISbesmMem3O4KXW2qvvHPyoqysFK7GV75BljfCIyAVgMRAKvGGOy7K7DLklJSXhiY/FW1NwriKg8jIjQrl07hytTSh3P4/HU+/xs50jbxxjzEfCRE+e2m4jQvVs31m4/QBUQUX6Qdu3b43a7nS5NKXWccA0CHVlsgz59+hBZfhACfqLLC+nbp4/TJSml6hEXF1fv87OdBoEN+vTpgwn4cB3Jw3jL6aNBoFRIio+PP/pcg0A1qd69ewMQlb/5mNdKqdBSNwjCqWefBoEN2rRpQ2xcHK7iPYgI3bp1c7okpVQ9wqkVUJcGgQ1EhM6dOwPQuk3bo2MLlFKhJTY21ukSHKFBYJMO6ekAdOrYoYE9lVJOCdfefBoENmnfvmY6JR0/oFToiogIzz+J4TOG2mHXXHMN7du3Z/DgwU6XopQ6ibjYWLr36OF0GbaSupMshaqMjAyTmZnpdBlKqTBQUlJCdHT0WXEvT0RWGWMyGtpPWwRKKVVHQkKC0yXYLjwviCmllDpKg0AppcKcBoFSSoU5DQKllApzGgRKKRXmNAiUUirMaRAopVSYaxYDykSkANjpdB1nkVZAodNFKFUP/dlsWp2MMQ0ukN4sgkA1LRHJbMxoQ6Xspj+bztBLQ0opFeY0CJRSKsxpEISnF50uQKkT0J9NB+g9AqWUCnPaIlBKqTCnQRBGRGSEiGwRkVwRmex0PUrVEpFXRCRfRDY4XUs40iAIEyISCfwVGAn0BW4Qkb7OVqXUUXOBEU4XEa40CMLH+UCuMWabMcYLzAeucrgmpQAwxvwfcNDpOsKVBkH4aA/srvM6L7hNKRXmNAjCh9SzTbuMKaU0CMJIHtChzut0YK9DtSilQogGQfj4F9BDRLqISDRwPbDQ4ZqUUiFAgyBMGGN8wARgMbAJWGCMyXK2KqVqiMibwLdALxHJE5Fbna4pnOjIYqWUCnPaIlBKqTCnQaCUUmFOg0AppcKcBoFSSoU5DQKllApzGgQqbIhIuoh8ICI5IrJVRGYGx1Sc7JgH7KpPKadoEKiwICICvAu8b4zpAfQE4oFHGjhUg0Cd9TQIVLi4FKg0xswBMMb4gXuAcSJyp4g8X7ujiCwSkf8QkWmAR0TWiMgbwa/dJCLrRGStiLwW3NZJRJYGty8VkY7B7XNFZJaIfC4i20Tk4uC8+5tEZG6d8/1MRL4VkdUi8r8iEm/b/4pSaBCo8NEPWFV3gzGmGNgFuOo7wBgzGagwxgwyxowRkX7AFOBSY8xAYGJw1+eBV40xA4A3gGfrvE0yNSF0D/BPYEawlv4iMkhEWgEPApcZYwYDmcC9TfENK9VY9f4CKHUWEuqfbfVE2+tzKfC2MaYQwBhTO3/+BcB/Bp+/BjxR55h/GmOMiKwHDhhj1gOISBbQmZrJ//oCX9dcvSKamqkWlLKNBoEKF1nA/6u7QUQSqZmR9QjHto7dJ3iPxoZG3X2qgo+BOs9rX7sAP7DEGHNDI95XKUvopSEVLpYCsSJyExxduvNpapZI3AYMEpEIEelAzWputapFJKrOe/xCRFoG3yMluP0bamZzBRgDfHUKda0ALhSR7sH3jBWRnqf6zSl1JjQIVFgwNbMrXgNcJyI5QDZQSU2voK+B7cB64ClgdZ1DXwTWicgbwdlaHwG+EJG1wPTgPr8FbhGRdcCN/HDvoDF1FQBjgTeDx68Aep/u96nU6dDZR5VSKsxpi0AppcKcBoFSSoU5DQKllApzGgRKKRXmNAiUUirMaRAopVSY0yBQSqkwp0GglFJh7v8DuwIS31tofOQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='BMI', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DiabetesPedigreeFunction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bf29cf8>"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.DiabetesPedigreeFunction, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a40ec18>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='DiabetesPedigreeFunction', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1bff6278>"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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50bhrkUbBoJe6A3S+QHcQmNQcg15Htf5cSufTnS72ir7tGUk+0J9L/PYkdyS5rL/v5Un+tT8R2mfnDs2XJplBr6PdpXTncf8P4JEkZwG/C6wDfo3uaMbz4BfnXvoH4LKqejlwHXDNOIqWlmJkPw4uTYnNdCdmg+7Q9M3AM4FPVdXPge8luau//3TgTODO7vQlHEN3Clppohn0Omr151C5EDgzSdEFd/HEOXCe8hDg/qo67wiVKA2FUzc6ml0GfKyqXlBV66pqLd0vNT0M/F4/V38ycEHffw8wk+QXUzlJXjKOwqWlMOh1NNvMU7feb6L7oYhZutMWf5DujKmPVtXP6P44vCfJV4F76c4xLk00z14pLSDJc6rqx/30zt10v0b1vXHXJS2Hc/TSwm7vfyzmWcBfG/KaZm7RS1LjnKOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/BtPpG+2qagEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data.Age, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1c205390>"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Outcome', y='Age', hue='Outcome', data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "有图可知，age对Outcome的关联度很高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1a383240>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data.hist(figsize=(16,16))  \n",
    "# data.plot(kind='box', subplots=True, layout=(3,3), figsize=(16,14))\n",
    "# sns.pairplot(data, hue='Outcome')\n",
    "\n",
    "corr = data.corr()\n",
    "plt.subplots(figsize=(16, 16))\n",
    "sns.heatmap(corr, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 空缺值填补"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pregnancies                   0\n",
       "Glucose                       5\n",
       "BloodPressure                35\n",
       "SkinThickness               227\n",
       "Insulin                     374\n",
       "BMI                          11\n",
       "DiabetesPedigreeFunction      0\n",
       "Age                           0\n",
       "Outcome                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_columns = ['Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI']\n",
    "### 把0值转成NaN\n",
    "data[empty_columns] = data[empty_columns].replace(0, np.NaN)\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[empty_columns] = data[empty_columns].fillna(data[empty_columns].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ### 特征工程\n",
    "# from sklearn.feature_selection import SelectKBest #移除那些除了评分最高的 K 个特征之外的所有特征\n",
    "# from sklearn.feature_selection import chi2\n",
    "\n",
    "# Y = data['Outcome']\n",
    "# X = data.drop('Outcome', axis=1)\n",
    "\n",
    "# select_X = SelectKBest(score_func=chi2, k =4)\n",
    "# fit = select_X.fit(X, Y)\n",
    "\n",
    "# new_X = fit.transform(X) \n",
    "# new_X[0:5]\n",
    "# ### 由图可知表现最佳的特征是：Glucose葡萄糖、Insulin胰岛素、BMI指数、Age年龄\n",
    "\n",
    "# X = pd.DataFrame(data=new_X, columns=['Glucose', 'Insulin', 'BMI', 'Age'])\n",
    "# X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Standardization标准化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = data['Outcome']\n",
    "X = data.drop('Outcome', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.63994726,  0.86604475, -0.03198993, ...,  0.16661938,\n",
       "         0.46849198,  1.4259954 ],\n",
       "       [-0.84488505, -1.20506583, -0.5283186 , ..., -0.85219976,\n",
       "        -0.36506078, -0.19067191],\n",
       "       [ 1.23388019,  2.01666174, -0.69376149, ..., -1.33250021,\n",
       "         0.60439732, -0.10558415],\n",
       "       ...,\n",
       "       [ 0.3429808 , -0.02157407, -0.03198993, ..., -0.910418  ,\n",
       "        -0.68519336, -0.27575966],\n",
       "       [-0.84488505,  0.14279979, -1.02464727, ..., -0.34279019,\n",
       "        -0.37110101,  1.17073215],\n",
       "       [-0.84488505, -0.94206766, -0.19743282, ..., -0.29912651,\n",
       "        -0.47378505, -0.87137393]])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "ss_x = StandardScaler()\n",
    "X = ss_x.fit_transform(X) \n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 模型训练"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 分开训练集和测试集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.20, random_state=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Logistic 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr= LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.48531493 0.46318256 0.54462155 0.40936432 0.49826231]\n",
      "cv logloss is: 0.4801491343642157\n"
     ]
    }
   ],
   "source": [
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省是采用StratifiedKFold\n",
    "from sklearn.cross_validation import cross_val_score\n",
    "loss = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print('logloss of each fold is: ',-loss)\n",
    "print('cv logloss is:', -loss.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 正则化的 Logistic Regression及参数调优\n",
    "logistic回归的需要调整超参数有：C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "\n",
    "目标函数为：J = sum(logloss(f(xi), yi)) + C* penalty \n",
    "\n",
    "在sklearn框架下，不同学习器的参数调整步骤相同：\n",
    "1. 设置候选参数集合\n",
    "2. 调用GridSearchCV\n",
    "3. 调用fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_params_: {'penalty': 'l1', 'C': 1} best_score_: 0.47911196634159975\n"
     ]
    }
   ],
   "source": [
    "### 按neg_log_loss评价\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penalty = penaltys, C = Cs)\n",
    "\n",
    "lr_penalty= LogisticRegression()\n",
    "grid= GridSearchCV(lr_penalty, tuned_parameters,cv=5, scoring='neg_log_loss')\n",
    "grid.fit(X_train,y_train)\n",
    "\n",
    "print('best_params_:', grid.best_params_, 'best_score_:', -grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果最佳值在候选参数的边缘，最好再尝试更大的候选参数或更小的候选参数，直到找到拐点。\n",
    "l1, c=1, 错误率0.47911196634159975"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [],
   "source": [
    "# grid.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('mean_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n",
      "/Users/admin/Documents/Anaconda5.2/anaconda3/lib/python3.5/site-packages/sklearn/utils/deprecation.py:122: FutureWarning: You are accessing a training score ('std_train_score'), which will not be available by default any more in 0.21. If you need training scores, please set return_train_score=True\n",
      "  warnings.warn(*warn_args, **warn_kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot CV误差曲线\n",
    "test_means = -grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "train_means = -grid.cv_results_[ 'mean_train_score' ]\n",
    "train_stds = grid.cv_results_[ 'std_train_score' ]\n",
    "\n",
    "\n",
    "# plot results\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    plt.errorbar(x_axis, train_scores[:,i], yerr=train_stds[:,i] ,label = penaltys[i] +' Train')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由图可知：\n",
    "- 训练集上，C值越大性能越好\n",
    "- 测试集上，C=1时性能最好\n",
    "- L1和L2的情况一样，损失率都比较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用LogisticRegressionCV实现正则化的 Logistic Regression\n",
    "### L1正则"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: array([[-0.69314718, -0.66877787, -0.48429085, -0.48399127, -0.48548122,\n",
      "        -0.48565946, -0.48567175],\n",
      "       [-0.69314718, -0.66799368, -0.4784401 , -0.46256591, -0.46363956,\n",
      "        -0.4637826 , -0.46379291],\n",
      "       [-0.69314718, -0.66742612, -0.52528509, -0.5429954 , -0.54712908,\n",
      "        -0.54750243, -0.54754921],\n",
      "       [-0.69314718, -0.67636932, -0.43210588, -0.40967194, -0.40811566,\n",
      "        -0.40797225, -0.40795824],\n",
      "       [-0.69314718, -0.66805987, -0.49006084, -0.4958569 , -0.49811276,\n",
      "        -0.49835648, -0.49838487]])}\n",
      "[[ 0.41616504  1.04535541 -0.00654103  0.12464473 -0.01911036  0.52445886\n",
      "   0.35995937  0.06858596]]\n",
      "accuracy: 0.7792207792207793\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "# LogisticRegressionCV比GridSearchCV快\n",
    "lrcv_L1 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l1', solver='liblinear', multi_class='ovr')\n",
    "lrcv_L1.fit(X_train, y_train)\n",
    "\n",
    "print(lrcv_L1.scores_)\n",
    "print(lrcv_L1.coef_)\n",
    "accuracy = lrcv_L1.score(X_test, y_test)\n",
    "print(\"accuracy: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_Cs = len(Cs)\n",
    "n_classes = 9\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "for j in range(n_classes):\n",
    "#     print(lrcv_L1.scores_[j])\n",
    "    scores[j][:] = np.mean(lrcv_L1.scores_[1], axis = 0)\n",
    "    \n",
    "mse_mean = np.mean(scores, axis = 0)\n",
    "plt.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，C为0.1的时候，L1正则损失最小"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### L2正则  LogisticRegressionCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{1: array([[-0.59592712, -0.50437944, -0.4820002 , -0.48491861, -0.48559038,\n",
      "        -0.48566332, -0.48567319],\n",
      "       [-0.59237619, -0.49483494, -0.46232601, -0.46321137, -0.46372849,\n",
      "        -0.46378943, -0.46379453],\n",
      "       [-0.60138242, -0.53362154, -0.53268466, -0.54509294, -0.54729915,\n",
      "        -0.54753731, -0.54756034],\n",
      "       [-0.58984227, -0.47681829, -0.41932648, -0.40917406, -0.40807747,\n",
      "        -0.40796861, -0.40795719],\n",
      "       [-0.60173736, -0.52671478, -0.5014128 , -0.4986305 , -0.49840577,\n",
      "        -0.49838498, -0.49838709]])}\n",
      "[[0.36007599 0.92048752 0.00640146 0.14798217 0.01460277 0.46863526\n",
      "  0.3254085  0.10711529]]\n",
      "accuracy: 0.7662337662337663\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "lrcv_L2 = LogisticRegressionCV(Cs=Cs, cv = 5, scoring='neg_log_loss', penalty='l2', multi_class='ovr')\n",
    "lrcv_L2.fit(X_train, y_train)  \n",
    "\n",
    "print(lrcv_L2.scores_)\n",
    "print(lrcv_L2.coef_)\n",
    "accuracy = lrcv_L2.score(X_test, y_test)\n",
    "print(\"accuracy: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_Cs = len(Cs)\n",
    "n_classes = 9\n",
    "scores =  np.zeros((n_classes,n_Cs))\n",
    "\n",
    "for j in range(n_classes):\n",
    "#     print(lrcv_L1.scores_[j])\n",
    "    scores[j][:] = np.mean(lrcv_L2.scores_[1],axis = 0)\n",
    "    \n",
    "mse_mean = np.mean(scores, axis = 0)\n",
    "plt.plot(np.log10(Cs), mse_mean.reshape(n_Cs,1)) \n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('neg-logloss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，C为0.1的时候，L2正则损失最小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_params_: {'penalty': 'l1', 'C': 0.1} best_score_: 0.7671009771986971\n"
     ]
    },
    {
     "data": {
      "image/png": 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2HhgP0KdP9KMBUlp1ZXiy3wRTvrR7E1BdTcPb5OR99i0/fzD0O3H3q4HYJJDdBdKavlnkty++x7bKan56zhDd5yCSwqJMFiUEndO7FABr9lJ3LHBdvW3fjWnCeho4jnrJwt3vA+4DKCwsjH6gcSpaOx/e/RssmBZcQaRlBp3IXXrDISN2P+HHJoHcbkH7fhK9t34bf3vrYy4b3ZfBBzc8xFVEUkOUyaIIGGhm/YHVBAnhq/UrmdlgIA94o962eWaW7+6lwOnAnAhjbVnKN8HCx+Hdv8K6BUH/wBHnwSfLILszXDMj2RHG5e7c+uwS2melc8NZg5IdjojEEVmycPcaM5sAzCIYOvuAuy82s0nAHHefHlYdB0x1/2ymH3evNbP/Bl6yoG3iHeD+qGJtEepqofjV4Cpi2bPBDV89h8MXfwVHXhQ0KbWgp/29svwT/r1iAz87dwhd22clOxwRiSPS+yzcfQYwo17ZxHrrP9/Lti8AR0UWXEux6QOY90jwKisJkkLhNTDiMujZMv95qmvruO3ZpRya356vHd832eGISAJ0B3cqqq6Apc8EzUwfzAYMBpwOZ98Gg78IGe2SHeEBmfLGRxRv2MEDVxXqGRUiLYSSRapwhzVzg2amhU/Azq2Q1w9O+ymMGAedC5IdYZPYtKOK3734HicPyue0wRoqK9JSKFkk244NsOCxIEl8sgQycmDIBTDycuh7QiTDVZPp7hfeY0dVLT875wgNlRVpQZQskqG2Bt5/OWhmWj4T6qqDm9zO/S0M+3IwoqkVWr5uG39/6yOuOK4vA/cyG6yIpCYli+a08f3gCmL+o7BtbXAz3Ohrg87qg1rm9NWJ2jVUtmN2Jt87U0NlRVoaJYuoVe2AxU8HSeLj/4ClwcDPwxfvgoFnQ0bbGDb60tJPeG3lBm4+bwh5Gior0uIoWUTBHUqKgmamRU8Gd1R3HQBn3AzDx0GnnsmOsFlV1dRx+4ylDMhvz+XHaaisSEukZNGUtq2HBVODq4gN7wVTZg+9MOis7nNc5FNnp6opb3zIBxt28ODVx2qorEgLpWRxoGqrYcULQYJ475/gtdB7NJz/Bxj6pWD21TZs4/ad/O6lFZw6WENlRVoyJYvGKl0edlZPhR2fQPse8LkJMOJyyFcH7i6/eeE9yqtq+ek5rbsDX6S1SyhZmNkJwDx332FmlwNHA79z948ijS7VVJbB4qeCJFHyNqRlwKAxQTPTYWcmfRbXVLN0bRmPvv0xXzu+H4f16JDscETkACR6ZfEnYLiZDQd+SDBV+BTglKgCSxnu8PEbMPevsORpqC6H7oPhrFth+FjooKaVhrg7tz23hE45mXzvzIHJDkdEDlCiyaLG3d3MLiC4oviLmV0ZZWBJV7YmuB/i3b/BpmLI6ghHXgwjr4CCwjbbWZ2oF5as5/WVG7nl/KF0ydVQWZGWLtFksc3MfgRcDpxsZulA62tzqamC92YGCWLli+B1wZQbJ98YTMGR1T7ZEbYIO2tquX3GUgb26MBlo9v4EwxFWolEk8WlBA8u+rq7rzOzPsBd0YXVzKp2wPb18JvDoXwjdOwJJ94Q3FndbUCyo2txHv7Ph3y0sZyHrxlFhobKirQKCV9ZEDQ/1ZrZIOBw4NHowmpGm4ph7buAwZDzg2amAadDWnqyI2uRNmzfyf++tJLTD+/BKYPykx2OiDSRRJPFbOAkM8sDXiJ4xOmlwGVRBdZsuh4K3QYFDxW6ZEqyo2nxfv38e1RU1/KTc45Idigi0oQSbSMwdy8Hvgz8r7tfCAyNLqxm1qGHhr02gSVrynisKBgqOyBfQ2VFWpOEk4WZHU9wJfFcWBa3ncbMxpjZcjNbaWY3NfD+3WY2L3y9Z2Zb6r3fycxWm9kfEoxTksTdmfTsYjrnZPLdMzRUVqS1SbQZ6nvAj4Cn3H2xmR0KvLKvDcIRU/cAZwElQJGZTXf3JbvquPsNMfWvB0bW+5hbgX8lGKMk0azF63mzeBO3XjCUzrm6ShNpbRK6snD3f7n7+cAfzayDuxe7+3fibDYKWBnWrQKmAhfso/44YjrNzewY4CDg+URilOTZWVPLHTOWMuigDowbpaGyIq1RQsnCzI40s3eBRcASM3vHzOL1WfQCVsWsl4RlDX1+X6A/8HK4ngb8GrgxTlzjzWyOmc0pLS1N5FAkAg++/iEfbyrnZ+cO0VBZkVYq0b/se4Hvu3tfd+8D/AC4P842Dd3i7HupOxZ43N1rw/X/Ama4+6q91A8+zP0+dy9098L8fA3TTIbSbTv5w8srOfOIHpw0UL8DkdYq0T6L9u7+aR+Fu79qZvFuZy4BesesFwBr9lJ3LHBdzPrxBEN1/wvoAGSZ2XZ336OTXJLr188vZ2dNLT/RrLIirVqiyaLYzH4G/DVcvxz4IM42RcBAM+sPrCZICF+tX8nMBgN5wBu7ytz9spj3rwIKlShSz6LVW3lsziq+fkJ/+nfXVCgirVmizVDXAPnAk8BT4fLV+9rA3WuACcAsYCkwLRxJNcnMzo+pOg6Y6u57a6KSFBQMlV1CXm4W12uorEirl9CVhbtvBuKNfmpouxnAjHplE+ut/zzOZzwEPLS/+5Zo/XPROt7+YBO3fWkYnXM0VFaktdtnsjCzZ9h7pzThcFppYyqrg1llDz+4I2OP7R1/AxFp8eJdWfyqWaKQFuWB1z+gZHMFj3xjtIbKirQR+0wW7q67p2U3n5RVcs/LKzlryEF87rDuyQ5HRJpJos/gXsiezVFbCWafvc3dNzZ1YJKa7pq1nKraOn7yRc0qK9KWJDp0diZQCzwSro8luOluK0Hn83lNHpmknIUlW3l8bgnfPOlQ+mmorEibkmiyOMHdT4hZX2hmr7v7CWZ2eRSBSWrZNats19wsJpx+WLLDEZFmlmiy6GBmo939LQAzG0VwZzVATSSRSUqZsXAdRR9u5o4Lj6RTdvRDZYf27Bz5PkQkcYkmi28AD5hZB4LmpzLg6+GUH/8vquAkNVRWB7PKHtGzE5dqqKxIm5ToTXlFwJFm1pngqXmxDymaFklkkjL+8toHrN5SwV0XH0V6WkPzQ4pIa5foFOWdzew3BM/fftHMfh0mDmnl1pdVcs8rKzl76EF8boCGyoq0VYneUfUAsA24JHyVAQ9GFZSkjl/+czk1tc6PNVRWpE1LtM9igLt/JWb9FjObF0VAkjoWlGzhibklXHvKofTtpqGyIm1ZosmiwsxOdPfXAMzsBKAiurCkUa5+rsk+yt2Z9MwSundox4TTNFRWpK1LNFl8G3h4Vwc3sAm4KqqgpHEuvTd4JMhj1x5/wJ/17IK1zPloM7/4ypF0bIahsiKS2hIdDTUPGG5mncL1skijkqSqrK7lzpnLGNKzExcdo6GyIhJ/ivLv76UcAHf/TQQxSZLdN7uY1Vsq+PUlwzVUtglM6nYXAI8lOY6mMPTHryU7hCbRWo4Dmu9Y4l1ZdGyWKCRlrNtayZ9efZ8vDDuY4w7tluxwWoWmaBYUSbZ4U5TfciAfbmZjgN8B6cBkd7+z3vt3A6eFq7lAD3fvYmYjgD8BnQgmMLzd3VvDF7OU98t/LqO2LgWGyjZhZ72IHLhEO7g/ZWZz3f3oBOqlA/cAZwElQJGZTXf3JbvquPsNMfWvB0aGq+XA19x9hZkdArxjZrPq3TkuTWzeqi08+e5qvn3qAHp3zU12OCKSQhrzmLNEG7FHASvdvdjdq4CpwAX7qD8OeBTA3d9z9xXh8hrgEyC/EbFKgoKhsovJ79iO6zRUVkTqaUyySLR9oBewKma9JCzbg5n1BfoDLzfw3iggC3h//8KU/TF9/hrmfryFG88eTId2+33BKSKt3H4nC3f/aYJVG7oCqf+0vV3GAo+7e+1uH2DWE/grcLW71+2xA7PxZjbHzOaUlpYmGJbUV1EVDJUd1qsTFx1dkOxwRCQFJTqR4DYzK6v3WmVmT5nZoXvZrASIHaRfAKzZS92xhE1QMfvsRHAV81N3f7Ohjdz9PncvdPfC/Hy1UjXWvbPfZ+3WSiaeO5Q0DZUVkQYk2t7wG4IT/SMEVwxjgYOB5QSTDJ7awDZFwEAz6w+sDrf5av1KZjYYyAPeiCnLAp4Cprj7/yUYozTC2q0V/Plf73POkT0Z1b9rssMRkRSVaLIY4+6jY9bvM7M33X2Smf24oQ3cvcbMJgCzCIbOPuDui81sEjDH3aeHVccBU909tonqEuBkoJuZXRWWXRXeSd702vAwzV/MXEadw01fODzZoYhICks0WdSZ2SXA4+H6RTHv7a0fAnefAcyoVzax3vrPG9jub8DfEoxNGmnux5t5et4arjtNQ2VFZN8S7eC+DLiCYAjr+nD5cjPLASZEFJtEqK4umFW2R8d2/NepGiorIvuW6ESCxcB5e3m79Uyy0ob8Y/5q5q3awq8uHk57DZUVkTgSHQ01yMxeMrNF4fpRZpboEFpJMeVVNfxi5nKOKujMl0c2eOuLiMhuEm2Guh/4EVAN4O4LCEY3SQv0538Vs66skonnDtFQWRFJSKLJItfd365XVtPUwUj0Vm+p4N5/vc+5R/WksJ+GyopIYhJNFhvMbADhyCczuwhYG1lUEplfzFwGwI+SPausiLQoifZsXgfcBxxuZquBDwhGSEkL8s5Hm5g+fw3fOf0wenXJSXY4ItKCJJosVgMPAq8AXYEy4EpgUkRxSRPbNVT2oE7tuPaUAckOR0RamESTxT+ALcBc9j6/k6Swp95dzfySrfzmEg2VFZH9l+hZo8Ddx0QaiURmx84afjlrGcN7d+FLIzRUVkT2X6Id3P8xsyMjjUQi8+d/vc/6sp0aKisijZbolcWJwFVm9gGwk2DmWXf3oyKLTJpEyeZy7ptdzPnDD+GYvnnJDkdEWqhEk8UXIo1CInPnzGWYaVZZETkwic4N9VHUgUjTK/pwE88uWMt3zxjIIRoqKyIHoDHP4JYWYNdQ2YM7ZXPtKXt7mKGISGKULFqpJ+aWsHD1Vm76wuHkZmmorIgcGCWLVigYKrucEb27cP7wQ5Idjoi0AkoWrdAfX11J6bad3HyehsqKSNOINFmY2RgzW25mK83spgbev9vM5oWv98xsS8x7V5rZivB1ZZRxtiaV1bXc/+8PuHBkL0b20VBZEWkakTVmm1k6cA9wFlACFJnZdHdfsquOu98QU/96YGS43BW4GSgkmOn2nXDbzVHF21qs2lxBuhk/HDM42aGISCsS5ZXFKGCluxe7exUwFbhgH/XHAY+Gy2cDL7j7pjBBvABoupE4tpRXsWlHFd86ZQA9O2uorIg0nSiTRS9gVcx6SVi2BzPrC/QHXt6fbc1svJnNMbM5paWlTRJ0S7VsXRkrP9lBTmY640/WUFkRaVpRJouGelZ9L3XHAo+7e+3+bOvu97l7obsX5ufnNzLMlm99WSXXPFhEWhoMPrgDOVnpyQ5JRFqZKJNFCdA7Zr2AvU9vPpbPmqD2d9s2bfvOGq5+sIitFdUMPqgj7TKUKESk6UWZLIqAgWbW38yyCBLC9PqVzGwwkAe8EVM8C/i8meWZWR7w+bBMYtTU1jHhkbksX7+NP1x2tJ5TISKRiSxZuHsNMIHgJL8UmObui81skpmdH1N1HDDV3T1m203ArQQJpwiYFJZJyN2ZOH0xry4v5dYLhnHa4B7JDklEWrFIv4q6+wxgRr2yifXWf76XbR8AHogsuBbu3tnFPPLWx3z71AF8dXSfZIcjIq2c7uBugZ6Zv4Y7Zy7jvOGHcOPndT+FiERPyaKFKfpwEz+YNp9j++Vx10VHaToPEWkWShYtSHHpdr45ZQ4FeTncd0Uh2Zka+SQizUPJooXYuH0nVz1YRLoZD159LHnts5Idkoi0IRpr2QJUVtfyjSlzWF9WyaPjj6Nvt/bJDklE2hglixRXV+fc8Ng85q3awp8uO5qjNZOsiCSBmqFS3B0zljJz0Tp+8sUjGDOsZ7LDEZE2SskihT38nw+Z/NoHXPW5fnz9xP7JDkdE2jAlixT14pL13PLMYs484iB+du4QzDREVkSSR8kiBS0o2cL1j77LsF6d+f24EaTrXgoRSTIlixSzalM51zw0h67ts5h8ZSG5WRqDICLJpzNRCtlaXs3VDxVRVVPL1PGj6dExO9mFnWElAAAM10lEQVQhiYgAShYpo6qmjmv/NoePNu5gyjWjOaxHx2SHJCLyKSWLFODu3PTEAt4s3sTdlw7n+AHdkh2SiMhu1GeRAu5+cQVPvruaH5w1iAtHFiQ7HBGRPShZJNm0Oav4/UsruKSwgAmnH5bscEREGqRkkUSvrdjAj59cyEkDu3P7hUfqXgoRSVmRJgszG2Nmy81spZndtJc6l5jZEjNbbGaPxJT/Mixbama/t1Z2Jl22roxv/+0dDuvRgXsuO5rMdOVtEUldkXVwm1k6cA9wFlACFJnZdHdfElNnIPAj4AR332xmPcLyzwEnAEeFVV8DTgFejSre5rS+rJJrHiwit106D1x1LJ2yM5MdkojIPkX5dXYUsNLdi929CpgKXFCvzjeBe9x9M4C7fxKWO5ANZAHtgExgfYSxNpvtO2u4+sEitlZU88BVx3JIl5xkhyQiEleUyaIXsCpmvSQsizUIGGRmr5vZm2Y2BsDd3wBeAdaGr1nuvjTCWJtFTW0d1z8yl+Xrt/GHy45m6CGdkx2SiEhCorzPoqE+Bm9g/wOBU4EC4N9mNgzoDhwRlgG8YGYnu/vs3XZgNh4YD9CnT5+mizwC7s7E6Yt5ZXkpd1x4JKcN7pHskEREEhbllUUJ0DtmvQBY00Cdf7h7tbt/ACwnSB4XAm+6+3Z33w7MBI6rvwN3v8/dC929MD8/P5KDaCr3zi7mkbc+5lunDOCro1M7sYmI1BdlsigCBppZfzPLAsYC0+vVeRo4DcDMuhM0SxUDHwOnmFmGmWUSdG632GaoZ+av4c6Zyzhv+CH88OzByQ5HRGS/RZYs3L0GmADMIjjRT3P3xWY2yczOD6vNAjaa2RKCPoob3X0j8DjwPrAQmA/Md/dnooo1SkUfbuIH/zefY/vlcddFR5Gm6cZFpAWKdG4od58BzKhXNjFm2YHvh6/YOrXAtVHG1hyKS7fzzSlzKOiSw31XFJKdmZ7skEREGkV3gkVk4/adXP1QEWlmPHj1seS1z0p2SCIijaZkEYHK6lq+MWUO67ZWMvnKQvp2a5/skEREDoimKG9idXXODY/NY96qLfzpsqM5uk9es+37sWuPb7Z9iUjboiuLJvb/Zi5l5qJ1/OSLRzBmWM9khyMi0iSULJrQlDc+5P5/f8CVx/fl6yf2T3Y4IiJNRsmiiby4ZD0/n76YM4/owcTzhmq6cRFpVZQsmsCCki1c/+i7DOvVmd+PG0m67qUQkVZGyeIArdpUzjUPzaFr+ywmX1lIbpbGDIhI66Mz2wHYWlHN1Q8VUVVTy9Txo+nRMTvZIYmIRELJopGqaur41l/f4aONO3j4mlEc1qNjskMSEYmMkkUjuDs3PbGAN4o3cvelw/ncgO7JDklEJFLqs2iEu19cwZPvrub7Zw3iwpEF8TcQEWnhlCz207Q5q/j9Syu4pLCA608/LNnhiIg0CyWL/fDaig38+MmFnHhYd26/8EjdSyEibYaSRYKWrSvj2397h8N6dOCPlx9NZrr+6USk7dAZLwHryyq55sEicrLSeeCqY+mUnZnskEREmpWSRRw7dtZwzUNFbK2o5oGrjuWQLjnJDklEpNlp6Ow+1NTWMeGRuSxbt43JVxYyrFfnZIckIpIUkV5ZmNkYM1tuZivN7Ka91LnEzJaY2WIzeySmvI+ZPW9mS8P3+0UZa33uzs3TF/PK8lImXTCU0wb3aM7di4iklMiuLMwsHbgHOAsoAYrMbLq7L4mpMxD4EXCCu282s9gz8hTgdnd/wcw6AHVRxdqQe2cX8/e3PuZbpwzgstF9m3PXIiIpJ8ori1HASncvdvcqYCpwQb063wTucffNAO7+CYCZDQEy3P2FsHy7u5dHGOtunl2whjtnLuPco3ryw7MHN9duRURSVpTJohewKma9JCyLNQgYZGavm9mbZjYmpnyLmT1pZu+a2V3hlcpuzGy8mc0xszmlpaVNEvScDzfx/WnzObZfHr+6eDhpmm5cRCTSZNHQWdbrrWcAA4FTgXHAZDPrEpafBPw3cCxwKHDVHh/mfp+7F7p7YX5+/gEHXFy6nW9MmUOvLjncd0Uh2Zl75CcRkTYpymRRAvSOWS8A1jRQ5x/uXu3uHwDLCZJHCfBu2IRVAzwNHB1hrGzcvpOrHyoizYyHrj6WvPZZUe5ORKRFiXLobBEw0Mz6A6uBscBX69V5muCK4iEz607Q/FQMbAHyzCzf3UuB04E5UQV68Z/+w9J1ZVTXOo+OP46+3dpHtSsRkRYpsiuL8IpgAjALWApMc/fFZjbJzM4Pq80CNprZEuAV4EZ33+jutQRNUC+Z2UKCJq37o4izrs5ZWbqd7Ttr+e2lIzi6T14UuxERadEivSnP3WcAM+qVTYxZduD74av+ti8AR0UZH8CHG3dQVllDn645fOHInlHvTkSkRWrzd3Afmt+Bo3p1JjNdo55ERPamzScLgKwMTZElIrIvShbAY9cen+wQRERSmr5Si4hIXEoWIiISl5KFiIjEpWQhIiJxKVmIiEhcShYiIhKXkoWIiMSlZCEiInEpWYiISFwWzOXX8plZKfDRAXxEd2BDE4WTTK3lOEDHkqpay7G0luOAAzuWvu4e9+lxrSZZHCgzm+PuhcmO40C1luMAHUuqai3H0lqOA5rnWNQMJSIicSlZiIhIXEoWn7kv2QE0kdZyHKBjSVWt5Vhay3FAMxyL+ixERCQuXVmIiEhcShYhM7vVzBaY2Twze97MDkl2TI1lZneZ2bLweJ4ysy7JjqmxzOxiM1tsZnVm1uJGrpjZGDNbbmYrzeymZMdzIMzsATP7xMwWJTuWA2Fmvc3sFTNbGv7f+m6yY2osM8s2s7fNbH54LLdEti81QwXMrJO7l4XL3wGGuPu3khxWo5jZ54GX3b3GzH4B4O7/k+SwGsXMjgDqgHuB/3b3OUkOKWFmlg68B5wFlABFwDh3X5LUwBrJzE4GtgNT3H1YsuNpLDPrCfR097lm1hF4B/hSS/y9mJkB7d19u5llAq8B33X3N5t6X7qyCO1KFKH2QIvNou7+vLvXhKtvAgXJjOdAuPtSd1+e7DgaaRSw0t2L3b0KmApckOSYGs3dZwObkh3HgXL3te4+N1zeBiwFeiU3qsbxwPZwNTN8RXLuUrKIYWa3m9kq4DJgYrLjaSLXADOTHUQb1QtYFbNeQgs9KbVWZtYPGAm8ldxIGs/M0s1sHvAJ8IK7R3IsbSpZmNmLZraogdcFAO7+E3fvDfwdmJDcaPct3rGEdX4C1BAcT8pK5FhaKGugrMVesbY2ZtYBeAL4Xr2WhRbF3WvdfQRBC8IoM4ukiTAjig9NVe5+ZoJVHwGeA26OMJwDEu9YzOxK4FzgDE/xjqn9+L20NCVA75j1AmBNkmKRGGH7/hPA3939yWTH0xTcfYuZvQqMAZp8EEKburLYFzMbGLN6PrAsWbEcKDMbA/wPcL67lyc7njasCBhoZv3NLAsYC0xPckxtXtgp/Bdgqbv/JtnxHAgzy9812tHMcoAziejcpdFQITN7AhhMMPLmI+Bb7r46uVE1jpmtBNoBG8OiN1vwyK4Lgf8F8oEtwDx3Pzu5USXOzL4I/BZIBx5w99uTHFKjmdmjwKkEM5yuB252978kNahGMLMTgX8DCwn+3gF+7O4zkhdV45jZUcDDBP+/0oBp7j4pkn0pWYiISDxqhhIRkbiULEREJC4lCxERiUvJQkRE4lKyEBGRuJQsRPaDmW2PX2uf2z9uZoeGyx3M7F4zez+cMXS2mY02s6xwuU3dNCupTclCpJmY2VAg3d2Lw6LJBBPzDXT3ocBVQPdw0sGXgEuTEqhIA5QsRBrBAneFc1gtNLNLw/I0M/tjeKXwrJnNMLOLws0uA/4R1hsAjAZ+6u51AOHstM+FdZ8O64ukBF3mijTOl4ERwHCCO5qLzGw2cALQDzgS6EEw/fUD4TYnAI+Gy0MJ7kav3cvnLwKOjSRykUbQlYVI45wIPBrO+Lke+BfByf1E4P/cvc7d1wGvxGzTEyhN5MPDJFIVPpxHJOmULEQap6Hpx/dVDlABZIfLi4HhZravv8F2QGUjYhNpckoWIo0zG7g0fPBMPnAy8DbBYy2/EvZdHEQw8d4uS4HDANz9fWAOcEs4CypmNnDXMzzMrBtQ6u7VzXVAIvuiZCHSOE8BC4D5wMvAD8NmpycInmOxiOC54W8BW8NtnmP35PEN4GBgpZktBO7ns+ddnAa0uFlQpfXSrLMiTczMOrj79vDq4G3gBHdfFz5v4JVwfW8d27s+40ngRy34+ePSymg0lEjTezZ8IE0WcGt4xYG7V5jZzQTP4f54bxuHD0p6WolCUomuLEREJC71WYiISFxKFiIiEpeShYiIxKVkISIicSlZiIhIXEoWIiIS1/8HIPaWPCGLRHYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters=dict(penalty=penaltys,C=Cs)\n",
    "\n",
    "lr_penalty=LogisticRegression()\n",
    "grid=GridSearchCV(lr_penalty,tuned_parameters,cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"best_params_:\", grid.best_params_, \"best_score_:\", grid.best_score_)\n",
    "\n",
    "test_means = grid.cv_results_[ 'mean_test_score' ]\n",
    "test_stds = grid.cv_results_[ 'std_test_score' ]\n",
    "\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis, test_scores[:,i], yerr=test_stds[:,i] ,label = penaltys[i] +' Test')\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'neg-logloss' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参数C为l1，C为0.1，取得最佳的得分0.7671009771986971"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "SVC1 = LinearSVC().fit(X_train, y_train)\n",
    "SVC1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.78      0.89      0.83        97\n",
      "          1       0.75      0.58      0.65        57\n",
      "\n",
      "avg / total       0.77      0.77      0.77       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "y_predict = SVC1.predict(X_test)\n",
    "print(metrics.classification_report(y_test, y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7727272727272727\n"
     ]
    }
   ],
   "source": [
    "# print(metrics.confusion_matrix(y_test, y_predict))\n",
    "accuracy = SVC1.score(X_test, y_test)\n",
    "print(\"accuracy: {}\".format(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 线性SVM正则参数调优\n",
    "线性SVM LinearSVC的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和正则函数penalty（L2/L1） \n",
    "\n",
    "采用交叉验证，网格搜索步骤与Logistic回归正则参数处理类似，在此略。\n",
    "\n",
    "这里我们用校验集（X_test、y_test）来估计模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_grid_point_Linear(C, X_train, y_train, X_test, y_test):\n",
    "    SVC2 =  LinearSVC(C = C)\n",
    "    SVC2 = SVC2.fit(X_train, y_train)    \n",
    "    accuracy = SVC2.score(X_test, y_test)\n",
    "#     print(C)\n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.7662337662337663\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.7792207792207793\n",
      "accuracy: 0.7337662337662337\n",
      "accuracy: 0.5454545454545454\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#需要调优的参数\n",
    "C_s = np.logspace(-3, 3, 7)\n",
    "#penalty_s = ['l1','l2']\n",
    "# print(C_s)\n",
    "accuracy_s = []\n",
    "for i, oneC in enumerate(C_s):\n",
    "    tmp = fit_grid_point_Linear(oneC, X_train, y_train, X_test, y_test)\n",
    "    accuracy_s.append(tmp)\n",
    "\n",
    "x_axis = np.log10(C_s)\n",
    "plt.plot(x_axis, np.array(accuracy_s), 'b-')   \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuracy' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，SVM最优正则参数C为0.01，accuracy为0.7792207792207793"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "线性SVM与Logistic回归的结果是很接近的，在训练样本数量足够大而特征数较小的情况下，可以通过使用复杂核函数的SVM来获得更好的预测性能，而且因为训练样本数量并没有达到百万级，使用复杂核函数的SVM也不会导致运算过慢；"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 RBF核SVM正则参数调优\n",
    "\n",
    "RBF核是SVM最常用的核函数。\n",
    "RBF核SVM 的需要调整正则超参数包括C（正则系数，一般在log域（取log后的值）均匀设置候选参数）和核函数的宽度gamma\n",
    "C越小，决策边界越平滑； \n",
    "gamma越小，决策边界越平滑。\n",
    "\n",
    "采用交叉验证，网格搜索步骤与Logistic回归正则参数处理类似，在此略。\n",
    "\n",
    "这里我们用校验集（X_test、y_test）来估计模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.7922077922077922\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.7922077922077922\n",
      "accuracy: 0.8246753246753247\n",
      "accuracy: 0.7142857142857143\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.7727272727272727\n",
      "accuracy: 0.8051948051948052\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.8116883116883117\n",
      "accuracy: 0.7272727272727273\n",
      "accuracy: 0.6948051948051948\n",
      "accuracy: 0.6298701298701299\n",
      "accuracy: 0.6298701298701299\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "def fit_grid_point_RBF(C, gamma, X_train, y_train, X_test, y_test):\n",
    "    \n",
    "    SVC3 =  SVC( C = C, kernel='rbf', gamma = gamma)\n",
    "    SVC3 = SVC3.fit(X_train, y_train)\n",
    "    accuracy = SVC3.score(X_test, y_test)\n",
    "#     print(\"C, gamma:\", C, gamma)\n",
    "    print(\"accuracy: {}\".format(accuracy))\n",
    "    return accuracy\n",
    "\n",
    "#需要调优的参数\n",
    "C_s = np.logspace(-2, 2, 5)\n",
    "gamma_s = np.logspace(-2, 2, 5)  \n",
    "accuracy_s = []\n",
    "\n",
    "for i, oneC in enumerate(C_s):\n",
    "    for j, gamma in enumerate(gamma_s):\n",
    "        tmp = fit_grid_point_RBF(oneC, gamma, X_train, y_train, X_test, y_test)\n",
    "        accuracy_s.append(tmp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "有图可知，参数C为1, 核密度宽度gamma为0.1, accuracy:0.8246753246753247最大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best_params_: {'gamma': 0.0001, 'C': 1000} best_score_: 0.7703583061889251\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Cs=[0.001,0.01,0.1,1,10,100,1000]\n",
    "gammas=[0.0001,0.001,0.01,0.1,1]\n",
    "\n",
    "param_grid={'C':Cs,'gamma':gammas}\n",
    "grid=GridSearchCV(SVC(kernel='rbf'),param_grid,cv=5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(\"best_params_:\", grid.best_params_, \"best_score_:\", grid.best_score_)\n",
    "test_means = grid.cv_results_['mean_test_score' ]\n",
    "test_stds = grid.cv_results_['std_test_score' ]\n",
    "\n",
    "# plot 结果\n",
    "n_Cs = len(Cs)\n",
    "number_gamms = len(gammas)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_gamms)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_gamms)\n",
    "x_axis = np.log10(Cs)\n",
    "for i, value in enumerate(gammas):\n",
    "    plt.plot(x_axis, test_scores[:,i] ,label = gammas[i])\n",
    "    \n",
    "plt.legend()\n",
    "plt.xlabel( 'log(C)' )                                                                                                      \n",
    "plt.ylabel( 'accuary' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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